Replication of structured data records among partitioned data storage spaces

ABSTRACT

Systems, methods, and software for management of partitioned data storage spaces is provided herein. An exemplary method includes storing sets of structured data records among partitioned data storage spaces, with data fields of the structured data records correlated among the sets by relational associations. The method includes, in a data center that receives change actions related to the structured data records, selectively placing the change actions into a plurality of change feeds, where the change feeds can be commutatively executed in parallel to implement the change actions. The method further includes implementing the change actions and propagating replication data comprising the change actions in the plurality of change feeds.

RELATED APPLICATIONS

This application hereby claims the benefit of priority to U.S.Provisional Patent Application 62/267,307, titled “REPLICATION ORDERINGIN A CROSS-DOCUMENT EVENTUAL CONSISTENCY SYSTEM,” filed Dec. 15, 2015,and U.S. Provisional Patent Application 62/267,311, titled “IMMEDIATEAND EVENTUAL VERIFICATIONS IN MULTI-DOCUMENT REPLICATION,” filed Dec.15, 2015, which are hereby incorporated by reference in their entirety.

BACKGROUND

Data storage systems can include various data structures to hold andrelate data records, such as databases, tables, and other datastructures. Structured query languages (SQL) can be used in relationaldatabase management systems (RDBMS) to query various data structures.Non-relational databases, such as schemaless or NoSQL-type databases,allow for various flexibility as compared to SQL-based data. NoSQLdatabases can store data in one or more tables and use updatingprocesses which may not provide immediate data coherency throughout anentire database system. However, these NoSQL databases can be bettersuited for distributed storage systems, such as cloud storage systems,multi-data center systems, among other redundant and non-local datastorage systems.

OVERVIEW

Systems, methods, and software for management of partitioned datastorage spaces is provided herein. An exemplary method includes storingsets of structured data records among partitioned data storage spaces,with data fields of the structured data records correlated among thesets by relational associations. The method includes, in a data centerthat receives change actions related to the structured data records,selectively placing the change actions into a plurality of change feeds,where the change feeds can be commutatively executed in parallel toimplement the change actions. The method further includes implementingthe change actions and propagating replication data comprising thechange actions in the plurality of change feeds.

This Overview is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. It may be understood that this Overview is not intended toidentify key features or essential features of the claimed subjectmatter, nor is it intended to be used to limit the scope of the claimedsubject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the disclosure can be better understood with referenceto the following drawings. While several implementations are describedin connection with these drawings, the disclosure is not limited to theimplementations disclosed herein. On the contrary, the intent is tocover all alternatives, modifications, and equivalents.

FIG. 1 illustrates a data storage environment in an example.

FIG. 2 illustrates a method of handling data records in an example.

FIG. 3 illustrates views of data records in an example.

FIG. 4 illustrates handling promoted properties in data records in anexample.

FIG. 5 illustrates handling promoted properties in data records in anexample.

FIG. 6 illustrates change reminders in data records in an example.

FIG. 7 illustrates change reminders in data records in an example.

FIG. 8 illustrates replication ordering for data in an example.

FIG. 9 illustrates verification of data changes in an example.

FIG. 10 illustrates a data storage environment in an example.

FIG. 11 illustrates control of data centers in an example.

FIG. 12 illustrates a computing system suitable for implementing any ofthe architectures, processes, and operational scenarios disclosedherein.

DETAILED DESCRIPTION

Non-relational databases, such as schemaless or NoSQL-type databases,allow for various flexibility as compared to SQL-based data. NoSQLdatabases can store data in one or more tables and use “eventuallyconsistent” processes which may not provide immediate data coherencythroughout an entire database system. In the examples below, variousviews are pre-computed to store data records in one or more tables.These views can arrange a data set comprising data records intodifferent configurations, and can be stored in different datapartitions. The pre-computed views can speed up access to databases forresponse to queries, as compared to SQL-based databases which typicallyrequire ‘JOIN’ or other functions acting on a single data set or view.When changes are made to the data records in the NoSQL databasesdiscussed herein, such as altering existing data records, deleting datarecords, or adding new data records, the various views are updated toreflect the changes. The update process takes time to complete, and thevarious views eventually become consistent with one another responsiveto the changes. Therefore, changes made in one view might not propagatefully to all other views or data records immediately.

Various user-facing applications can employ these database systems, suchas software for project management, planning, task management,calendars, or other types of software. Any distributed data service canalso employ these database systems. The database systems containdatabases or data sets for which pre-computed views can be established.Although any data can be held in the associated databases, many of theexamples herein discuss the context of projects and tasks to illustratevarious enhanced features. In project management or task managementexamples, projects can include one or more tasks which are to becompleted for the project. Arrangements of the projects and tasks can beestablished using structured data records which are related to eachother using keyed data or other relationships.

A normalized data storage system is one in which only one copy of thedata is kept, and indices are built above it to facilitate queries. Bycontrast, in a denormalized data storage system, the entire document orparts of the document are replicated multiple times in ‘views,’ witheach replica indexed differently to support a corresponding query. Forexample, in a system representing projects and associated tasks, thesystem may serve the queries: “What Tasks are in this Project?” as wellas “What Tasks are assigned to this User?” To service these queries, adenormalized storage system could store views of “Tasks Indexed byProject” and “Tasks Indexed by Assigned User,” both of which would havethe entire task document.

This can lead to difficulties when developers using the system wish tocreate experiences that are logical “joins” of the data, which cannot beserved by a single index. In our example, such a query might be: “Whatare the names of the Projects for each Task assigned to this User?” In anormalized system, this is a straightforward query, because the indicescan be ‘joined,’ as normalized transactional systems are nearly alwayson “localized” (non-distributed) hardware to facilitate transactions.For example, they are usually on a single computer or perhaps even thesame physical hard drive.

Since denormalized storage systems are built for scale, they usuallyhave replicated indexed data spread over many machines in a distributedfashion. In a denormalized system, retrieving the tasks for a user candone as a single query to “Tasks Indexed by Assigned User,” but findingthe project name for each task is a lookup operation for each task, andsince the projects may be distributed across different physical nodes,running all of these queries may be expensive in terms of computationaland transactional effort.

Normalized systems can be undesirable because normalized systems have asingle transaction system, which becomes a bottleneck in large-scaledeployments. Other systems use denormalized storage, but build a newdenormalized index or view for every possible query. This can quicklygrow to a large number of possible queries. While these implementationscan be done in parallel, the amount of storage required increases cost,space, time, and can decrease storage and computing efficiency indistributed systems. A third implementation uses fan-out queries,achieving “joining” of data by reading all data from separate datastores. This slows down the servicing of a query, because the querytakes as long as the maximum duration of a sub-query. The associatedsystems also become less reliable as many queries need to besuccessfully executed.

The examples discussed herein greatly extend the scope of queries thatcan be serviced by a system by putting properties of an entity ontoother entities related by associations by using promoted data fields. Anassociation may be similar to containment, linking, membership, or anyother relationship. Putting the property onto another object is called“promoting the property” herein, and the properties themselves are“promoted properties.” The examples discussed herein also allow foraggregating information across relationships, such as maintaining thecount of the number of entities in a certain relationship, or taking thesum of the values of some field of entities in a relationship, amongother aggregated information. Properties like this can be called“aggregated properties.”

In a system that maintains task and project data, the tasks can bedenormalized with an associated project name, so that the project nameis available in any query where the task is available. Similarly, theproject can keep a count of the number of tasks that the projectcontains. In this way, the data is similar to “pre-joined” data beforethe data is deposited in the denormalized indices.

Promoted properties can be promoted ‘to’ an entity on each edit,creation, or deletion of the entity. Example processes include (1)reading the associated entities to find any properties that must bepromoted to the modified entity, (2) augmenting the entity with theproperties to be promoted, (3) copying the entity to all denormalizedcopies of the entity, (4) removing any obsolete copies of the entity,and (5) verifying that none of the associated entities have changedsince step 1. If they have, the process can return to step 1. Promotedproperties can be promoted ‘from’ an entity on each edit, creation, ordeletion of the entity. Example processes include (1) finding theentities which are no longer associated with the entity, (2) removingthe promotion from those entities, and (3) finding the entities whichare still associated with the entity, or newly associated with theentity, and (4) updating the entities with the modified properties.

Aggregated properties can be replicated on each edit, creation, ordeletion of an entity. Example processes include (1) finding newlyassociated entities, taking a promoted properties-specific exclusivelock on each, and adding the aggregate to those entities. The exclusivelock is necessary because only the modified entity is locked, and nouser-facing lock is taken on the associated entities at any point. Theexample process also includes (2) finding entities that have theirassociation removed by the edit, taking a promoted properties-specificexclusive lock on each, and subtracting the aggregate from thoseentities. Aggregate properties can pose special concerns forretriability of edits in storage systems, because retrying addition andsubtraction is not idempotent. To help with this problem, when the editis registered, a unique key can be associated with the edit. Beforeapplying the edit, the old value of the aggregate property can be storedwhen the exclusive lock is registered. Then, if a subsequent edit findsthe expired lock, it must restore the previous value before obtaining anew exclusive lock.

Various improvements to data record handling using promoted properties,and data center management technology may be appreciated from theimplementations herein. The ability of data center management systems tohandle data record queries with fewer individual queries provides forenhanced operation of any associated data records, databases, and userinteraction. Technical effects improve the functioning of computer andcomputer systems which might include databases, applications (such asproject management applications), and data storage systems. Thisprovides various technical effects and advantages of decreasing theresponse time of data storage systems, reducing processor load of datastorage systems, and increasing code execution efficiency withassociated applications.

As a first example of a data system, FIG. 1 is presented. FIG. 1illustrates data storage environment 100. Environment 100 includes aplurality of data centers 110-112 which can provide data services touser 101. User 101 can interface with data center 110 in this examplevia user interface 120. Data center 110 also includes logic 121 whichimplements instructions issued by users, such as user input by user 101.Logic 121 also manages data stored in one or more partitions 130 whichstore one or more sets of structured data records. Each of the datacenters in FIG. 1 can include similar elements as data center 110.

Partitions 130 include one or more data arrays, such as sets ofstructured data records, pre-computed views, rows of data, tables ofdata, or other data structures. In FIG. 1, an example data type is shownfor a project management configuration, although other configurationsare possible. Projects 131 can relate to any number of tasks 132.Further distinctions can be made, such as for individual users assignedto tasks on a particular project. Thus, many projects can be stored inindividual partitions, with each of the projects having properties heldin data fields which can relate or be correlated to other data fieldsassociated with users, tasks, or other items.

Data elements discussed herein, such as data records, can includeintrinsic fields, promoted fields, and aggregate fields, among others.For example, tasks 132 can each comprise intrinsic fields 133 andpromoted fields 134. Promoted data fields reference properties of datafields of at least another set of data records, and in some examplescomprise information or values included in a data record of the otherset of data records. Intrinsic fields relate to data fields of each taskwhich correspond to that particular task, such as task name or othertask properties. In contrast to promoted data fields, intrinsic datafields do not reference properties of data fields in another set of datarecords. Aggregate data can comprise computed fields, such as fieldswhich represent aggregated properties of other fields. In one example,an aggregate data value indicates a total number of project records ortotal number of task records, and is re-computed responsive to a changein quantity of task records or project records.

As mentioned above, promoted fields relate to data fields of othertables or data structures, such as projects 131. For example, each taskcan have one or more additional data fields which indicate a property ofprojects 131, such as a project name to which the particular task isassigned. When lookups or queries are performed to retrieve the tasks,the intrinsic fields and promoted fields are provided responsive to thelookup or query. This can save additional processing time and lookupdelays since a query for a task will also provide an indication forother ‘elevated’ properties or data fields from another foreign datastructure.

Returning to the elements of FIG. 1, data centers 110-112 can be locatedat different physical or geographic locations, and coupled over one ormore network links and packet networks. Data centers 110-112 eachcomprise computer processing systems and equipment to receive data,store data, and process data, among other operations discussed herein.Data centers 110-112 each can include communication or networkinterfaces, user interfaces, as well as computer systems,microprocessors, circuitry, cloud-based systems, or some otherprocessing devices or software systems, and can be distributed amongmultiple processing devices. Examples of data centers 110-112 can alsoinclude software such as an operating system, databases, utilities,drivers, networking software, and other software or data structuresstored on one or more computer-readable media. In some examples, datacenters 110-112 each include elements discussed below in FIG. 11.

The elements of FIG. 1 can comprise a cloud storage platform, such asused for storage and synchronization of data across various devices,storage systems, services, and other elements, including administration,maintenance, and development systems. The elements of FIG. 1 cancomprise execution platforms such platform as a service (PaaS) systemsor infrastructure as a service (IaaS) systems to provide virtualized anddistributed application-level services to end users. Example platformsfor cloud storage and application-level services include MicrosoftAzure®, Microsoft SharePoint®, Microsoft Office 365®, MicrosoftOneDrive®, Google Drive®, Apple iCloud®, and Dropbox™, among others.However, in at least the examples herein, various improvements arediscussed which provide for enhanced management of sets of data recordsin eventually consistent data systems.

User interface 120 receives user input over one or more network links.The user input is translated into one or more actions for execution bylogic 121 which can be interpreted by further elements, such asoperating systems or applications. A graphical or textual user interfacecan be presented to user 101 that comprises one or more graphical ortextual user interface elements which are presented to a user forinteracting with the elements of data centers 110-112, among other datacenters. Application programming interfaces (APIs) can also be providedby user interface 120 for receiving user input, user instructions,change actions, data, data records, queries, searches, or other userinput. User interface 120 can also include various output elements forindicating data records or operational results to a user.

Logic 121 includes processing circuitry and storage systems. Logic 121can comprise one or more microprocessors and other processing circuitrythat retrieves and executes software from storage systems. Logic 121 canbe implemented within a single processing device but can also bedistributed across multiple processing devices or sub-systems thatcooperate in executing program instructions. Examples of logic 121include general purpose central processing units, application specificprocessors, and logic devices, as well as any other type of processingdevice, combinations, or variations thereof. In some examples, portionsof logic 121 are physically separate from some elements of the datacenters and are included in remote servers, cloud-based processingsystems, or virtualized computing systems.

Partitions 130 are included on one or more data storage systems of eachdata center. The data storage systems can comprise any computer readablestorage media capable of storing data records. The data storage systemscan also include data structures which include one or more databases,tables, lists, set of data records, or other data structures, includingcombinations and variations thereof. The data storage systems caninclude volatile and nonvolatile, removable and non-removable mediaimplemented in any method or technology for storage of information, suchas computer readable instructions, data structures, program modules, orother data. In no case is the computer readable storage media apropagated signal.

A further discussion of promoted properties is included in FIG. 2. FIG.2 is a flow diagram illustrating an example method of operating theelements of claim 1. The operations of FIG. 2 can be performed byelements of data centers 110-112, such as user interface 120, logic 121,data storage elements, or other associated elements. In FIG. 2, datacenters 110-112 store (201) sets of structured data records among thepartitioned data storage spaces, with data fields of the structured datarecords correlated among the sets by one or more relationalassociations. In a specific example, FIG. 1 shows projects 131 and tasks132, where each project can have one or more tasks assigned thereto.Views of this data can be computed and stored in the associate datacenters, where the views comprise various representations of theunderlying data. Other data types and relationships are possible. Thestructured data records can comprise tables, databases, or otherstructured data. When tables are employed, record-and-field typedesignations are typically used, but row-and-column designations can beemployed in some examples.

Data centers 110-112 maintain (202) these data fields in sets ofstructured data records, such as a first set and a second set. The setsof structured data records can be stored by any of data centers 110-112,in any of the associated partitions. Thus, the sets of structured datarecords can be distributed among the various storage spaces provided byany of data centers 110-112. In some examples, such as when views areemployed, each view can be stored on a different partition or differentdata center. In other examples, a set of views is replicated across morethan one partition or data center for redundancy, failover, or to speedup access to the associated data.

The data fields comprise promoted data fields that reference propertiesof data fields of at least another set of structured data records. Insome examples, promoted data fields comprise information or valuesincluded in a data record of the other set of data records, but caninclude aggregate data. Aggregate data can comprise computed data, suchas data which represents aggregated properties of other fields. In oneexample, an aggregate data field indicates a total number of projectrecords or total number of task records, and is re-computed responsiveto a change in quantity of task records or project records.

Data centers 110-112 monitor for user interaction, such as user updatesto data maintained by data centers 110-112. In FIG. 1, an associateduser interface is employed to monitor the user interactions and toprovide graphical, text-based, or network-based user interfaces. Whenuser updates occur, data centers 110-112 determine (203) if the userupdates relate to promoted data fields. When the user updates do notrelate to promoted data fields, then data centers 110-112 implement(204) the updates and propagate the updates to any associated structureddata records.

When the user updates relate to promoted data fields, then data centers110-112 implement (205) a portion of the update in the second set ofstructured data records and propagate the update to associated promoteddata fields in the first set of structured data records along with anyintervening changes received for the associated promoted data fields.Intervening changes can occur when a user makes further changes to thedata that the promoted data fields reference, such as when a multi-userenvironment is provided by data centers 110-112. The intervening changescan be incorporated to ensure that updates made to the fields,properties, or data referenced by the promoted data fields are properlypropagated to the promoted data fields.

The usage of promoted data fields can advantageously reduce a quantityof queries that need to be performed to retrieve data or records.Moreover, the processing workload for data centers 110-112 is reducedwhen data employs promoted data fields. Specifically, a query for datarecords with promoted data fields will return the data included in thepromoted data fields. When the promoted data fields contain informationor properties of other views or data sets, then multiple queries tothose views or data sets can be avoided. In examples where promoted datafields are not employed, multiple queries or JOIN functions might berequired to return the data desired by a user.

A further discussion of promoted properties is included in FIG. 3. FIG.3 is logical diagram 300 illustrating relationships between various setsof structured data records, which in FIG. 3 are referred to asdocuments. Other logical groups of sets of structured data records canbe employed instead of sets of structured data records.

FIG. 3 illustrates a method of managing data coherency among partitioneddata storage spaces. The method includes storing sets of structured datarecords among partitioned data storage spaces, with fields of thestructured data records correlated among the sets by one or morerelational associations. The method also includes maintaining promoteddata fields in a second set of structured data records that referenceproperties of a first set of structured data records. Responsive to auser instruction to add a new entry into the first set, the methodincludes adding a model data record in the second set to hold a positionfor the new entry, establishing values for fields of the model datarecord that reflects the new entry, and propagating the values to themodel data record in the second set merged with any intervening changesto the promoted data fields that affect the model data record. Thevalues for fields of the model data record can be initially establishedin a journal data structure.

Another example method includes storing sets of structured data recordsamong the partitioned data storage spaces, with fields of the structureddata records correlated among the sets by one or more relationalassociations, and maintaining promoted data fields in at least a firstset of structured data records that reference properties of fields of atleast a second set of structured data records. Responsive to a userinstruction to update at least a field of a first structured data recordin the partitioned data storage spaces, the method includes determiningif the update relates to a promoted data field. Based on the update notrelating to the promoted data field, the method includes implementingthe update and propagating the update to any associated ones of thestructured data records. Based on the update relating to the promoteddata field, the method includes implementing a portion of the update inthe second set of structured data records and propagating the update toassociated promoted data fields in the first set of structured datarecords along with any intervening changes received for the associatedpromoted data fields.

In FIG. 3, document 301 includes a set of structured data records whichcomprises a listing of projects managed by a particular projectmanagement software environment. Three example projects, P1-P3, arelisted in document 301, with each project having intrinsic fieldsrelated to project identifiers (P1, P2, P3), intrinsic fields related toproject names (P1_(NAME), P2_(NAME), P3_(NAME)), and project detailswhich represent further intrinsic fields, such as project descriptions.

Project P1 is shown in a detailed view as being associated with at leasttwo tasks, namely tasks T1 and T2. These tasks can be included in afurther set of structured data records, namely document 302. Two exampletasks, T1-T2, are listed in document 302, with each task havingintrinsic fields related to task numbers (T1, T2), task names(T1_(NAME), T2_(NAME)), and task details which represent furtherintrinsic fields, such as task descriptions. Furthermore, each task indocument 302 includes at least one promoted field or promoted property,indicated by a PP designator. These promoted properties indicate a nameof a project to which the task is assigned, such as P1_(NAME), in thepresent example. Alternatively, a project identifier or other projectproperties can be included in tasks as a promoted property instead of aproject name. The promoted property in this example allows any query toretrieve a task or task properties to also automatically retrieve theproject name to which the task is assigned. In systems where promotedproperties are not employed, a first N number of queries or searches areperformed to find associated project names, followed by subsequentqueries or searches among associated projects, such as among document301. Advantageously, using promoted properties, fewer queries can beperformed and faster data retrieval and analysis can occur.

FIG. 3 also shows a brief procedure to add additional data records withpromoted properties. Specifically, operation ‘1’ in FIG. 3 shows an addtask operation, which can be initiated by a user, such as user 101 ofFIG. 1 or logic 121 of FIG. 1. This add task operation first promptslogic 121 to add a model or placeholder data record into document 302for new task 303. The promoted property is typically not immediatelyresolved or known during task addition, so the model data record iscreated with a blank promoted property. For example, a new task mightfirst be created and then that task is subsequently assigned to aproject. The project name, as the promoted property, can be included inthe new task once the task is assigned to a project. If an interveningchange, indicated by operation ‘2’ of FIG. 3, occurs, which might affectthe promoted property, then that change can be successfully incorporatedinto the new task record and data coherency between the project list andthe task list can be maintained. Specifically, if operation ‘2’comprises a rename operation for a project to which the new task isassigned, then the promoted property field of the new task data recordwill reflect the new project name once the project name is placed intothe promoted property field of the model data record created for the newtask in document 302. Operation ‘3’ reflects an update process for task203 to update the blank placeholder project name entry with the namefrom the intervening change in operation ‘2’—namely the new P1_(NAME).

Advantageously, queries to document 302 can be more efficient withpromoted properties. Specifically, operation ‘4’ indicates a query toretrieve document 302 or a subset of tasks in document 302. This querywill automatically return the project associated with each task due tothe use of promoted properties in the task records. A query for task T1will return properties/fields of task T1 along with a promoted propertyrelating to properties of another view or document, namely a projectname associated with task T1 in this example. Other queries can beperformed, such as one for new task 303 which will returnproperties/fields of task 303 along with a promoted property relating toa project name associated with task 303. Updates to the fields that arereferenced by the promoted properties is propagated to the promotedproperties fields and thus are reflected in queries to the task records.In examples where promoted properties are not employed, multiple queriesor JOIN functions might be required to return a project name associatedwith a task.

FIG. 4 illustrates further examples and operational scenarios ofpromoted properties in eventually consistent systems, such asillustrated in FIG. 1. In FIG. 4, user-editable properties are homed inone document. This is a way to have the properties show up in otherdocuments for read operations, so that a query that might requiremultiple reads can be done in a single read. For example, the title of aproject can be promoted (via usage of a promoted property field) to atask in that project, so that the project title is retrieved withoutadditional processing or time cost when the task is read.

In FIG. 4, a process is detailed in operational scenarios 400 and 401which (1) select properties of an entity are copied to other entitiesrelated by associations; (2) updates to those properties update thecopies on the related entities; and (3) properties are appropriatelyupdated as associations are created or broken. Specifically, operationalscenario 400 shows logical project document 410 and several views411-413, one of which contains project data (411). View 411 containsaugmented data, which in this case includes the total number of tasks inthe project, currently 0. This is an aggregated property that is not auser-settable property of the project and is instead calculated based onproperties of the project.

In operational scenario 401, Task1 is created in Project-A. Theaggregated Total_Tasks property is responsively calculated and updated.A lock is placed on Project-A during this process. The lock is only onthe aggregate properties, and user-editable properties are stilleditable during this lock. When Task1 is replicated to the respectiveviews, the augmenting property Parent_Title is computed and copied tothe respective views, such as illustrated for views 412 and 413.

FIG. 5 continues the example of FIG. 4. In operational scenario 500 inFIG. 5, a new project (Project-B) has been created in logical document410, and Task1 has been moved from Project-A to Project-B. Aggregateproperties are re-computed, and obsolete copies of Task1 in views areremoved.

Turning now to a discussion regarding propagating changes to data sets,FIGS. 6-9 will be discussed. In eventually-consistent data storagesystems, tradeoffs must be made between performance and functionalitywhen making edits to data records. When a user makes an edit to anentity, such as data records, the system must prevent another user frommaking a conflicting edit at the same time. This can be done with eitheroptimistic resource locking, where simultaneous edits result in aconflict error, or in pessimistic resource locking, where the user getsa resource lock first before submitting a change. In both cases, thescope of the resource lock determines the breadth of the edit. Forexample, in a system representing projects and their constituent tasks,when a task is updated, the system may take a resource lock at the tasklevel (a narrow resource lock) or at the scope of the entire project (abroad resource lock). A broad resource lock allows for sophisticatededits that span entities, but forces edits which might have beenparallel to instead be restricted to serial edits.

Narrow resource locks can provide for higher throughput than broadresource locks. However, narrow locks can hinder operations that cascadeacross documents, like cascading deletes. Relatedly, there are someedits which have follow-up work that must be done after a certain periodof time. For example, a service may wish to retain deleted documents forsome measure of time, such as a number of days, allowing for “recyclebin” or “trash” functionality, and permanently delete the item after ithas been in the recycle bin for several days. In many unindexed storagesystems, like key-value storage systems, finding items which have been“recycled” for a given amount of time can be very expensive.

Some systems use broader lock schemes and accept a throughput penalty,or use processes that scan all data records looking for states thatindicate work needs to be done. The examples herein provide enhancedoperations using reminders. When an edit is submitted, reminders can beadded to a “reminder list” to be followed up on later. A first examplereminder is “remove this data record from the recycle bin in 30 days, ifit is still there.” This first example reminder provides for deferringfollow-up work for user edits/actions. A second example reminder is “Ifthis Task is in a Project that has been deleted, delete the task.” Thissecond example reminder can help with cascading effects beyond thetransaction scope. The actions to be taken by the example reminders andassociated system allow for automated conflict resolution. For instance,in handling the rule “If this Task is in a Project that has beendeleted, delete the task,” the task deletion process can proceedregardless of any intervening edits to the task. Advantageously, thereminder list and associated operations can avoid data locking and datalocking throughput penalties.

The examples herein can provide enhanced operations for schedulingdeletion or purging of data records to meet regulatory and compliancerequirements around data retention. In some implementations, variouscertifications and levels of compliance where customers such asgovernments, regulated industries, and the like, desire that data ordata records be retained for ‘x’ amount of days and purged within ‘y’amount of days. Advantageously, the reminders and reminder listsemployed herein can provide these data record handling features forvarious customer needs in a NoSQL environment. Auditing of complianceprocedures can also be provided via the reminder lists which arequeryable to determine associated data retention and purging schedules.

The reminder list comprises a listing of work that is queryable by timeswhen the follow-up actions are scheduled to occur. To service queries,the reminder list might be associated with a single index, or might beassociated with an index in each shard of some partitioning of theentities being edited. A background process can periodically check theindex for reminders that should be executed now or within apredetermined time frame. This background process can evaluate the stateof the system, possibly across multiple documents, reach a decision onwhether to execute the rule, and then execute (or not execute ifappropriate). Once executed, the reminders can be deleted, and the nextreminders to be executed are always conveniently available from theindex.

The process executing reminders makes decisions regarding multipleobjects, but in the examples herein, does not enable locks on all of themultiple objects. In a first example that prevents multiple locks, thesystem might ensure that the objects being acted on are not editable dueto an associated state. For example, if an associated rule is to purgethe recycle bin, the system can ensure that no other processes can editentities in the recycle bin. In a second example that prevents multiplelocks, the system might put an optimistic concurrency tag (which can becalled an “eTag”) of the object into the reminder. If the eTag is nolonger valid, then the process executing reminders can skip theoperation or alert a user. For follow-up work, the process might make abest effort to submit an edit, and ignore possibly conflicting edits.

Various improvements to data manipulation operations, data set locking,and data center management technology may be appreciated from theforegoing examples. The ability of data center management systems tohandle data locking and data set changes efficiently usingreminder-based processing provides for enhanced operation of anyassociated data records, databases, and user interaction. Technicaleffects improve the functioning of computer and computer systems whichmight include databases, applications (such as project managementapplications), and data storage systems. This provides various technicaleffects and advantages by increasing the response time of data storagesystems and reducing processor load and more efficient code executionfor associated applications.

FIG. 6 illustrates an example method 600 of managing changes amongpartitioned data storage spaces using reminders. FIG. 6 illustratesreminders for processing in eventually consistent systems, and can beimplemented on any of the systems discussed herein, such as system 100in FIG. 1, system 1000 in FIG. 10, or system 1201 in FIG. 12, althoughvariations are possible. Method 600 includes storing (601) sets ofstructured data records among the partitioned data storage spaces, withfields of the structured data records correlated among the sets by oneor more relational associations. These data records can contain any dataor set of data. In many of the examples herein, projects and tasks arediscussed to provide exemplary data stored in the data records, withones of the tasks associated with the projects. It should be understoodthan other data, data type, or data relationships can be employed.

Method 600 includes also includes receiving (602) change actions relatedto the structured data records. The change actions can be received intoa user interface and interpreted by various processing systems or logicplatforms. The change actions can comprise edits, renaming, deletions,relations, or other operations for the data records. Responsive to thechange actions, the method includes establishing (603) data locks forportions of the structured data records based on the change actions toprevent further changes to the portions before execution of the changeactions. These data locks can be established for the data recordsindicated by the change actions. In some examples, the data locksprevent modification or intervening changes to the associated datarecords by other processes or by users while the change actions arepending. It should be noted that further data locks are not establishedfor further data records that reference the data records indicated inthe change actions. For example, when the change actions indicatemodifications to tasks, data locks might be placed on the tasks duringchange pendency for the tasks, but further data locks are not placed onprojects that might reference or contain those tasks. Intervening editsto the projects can occur.

The method also includes scheduling execution (604) of the changeactions in an execution index or reminder index. In some examples, oncethe change actions are scheduled, any associated data locks are removedto allow for intervening edits or changes to the associated data recordswhile the change actions are pending in the reminder index. However,other examples can place data locks on the associated data records whilethe change actions are pending. Timing information, such a timer or timedelay, can indicate when to execute the change actions, and this timinginformation is indicated in the change index for each of the associateddata records. While the change actions are pending in the reminderindex, intervening changes for at least further ones of the structureddata records are allowed (605), such when the further ones of the datarecords reference or contain the data records indicated in the reminderindex. These intervening changes can be resolved upon execution of theassociated change actions. Change actions can be executed periodically(606), such as according to timing information stored with the changeactions in the reminder index. Conflicts between change actions can beresolved without interaction of any original change submitter, such as auser or other data system.

In addition to the change actions for the data records indicated by thechange actions, one or more rules can be established in the reminderindex to affect other data records. For example, when tasks areindicated in the change actions, associated projects can have rulesapplied thereto when execution of the change actions for the tasks. Inthis manner, projects and associated data records are not locked and canreceive further user changes during pendency of the associated tasks inthe reminder index. The rules can include performing actions on thefurther or other data records once the change actions are executed forassociated data records indicated in the reminder index. In theproject-task regime, when change actions are pending for ones of thetasks, then rules can be established and included with the changeactions in the reminder index. These rules can include modifying (suchas deleting) projects which reference the tasks upon execution of thechange actions for the tasks. Conversely, the rules can includemodifying (such as deleting) tasks associated with a project uponexecution of the change actions for the project. For example, when aproject is deleted, rules can indicate that tasks that are associatedwith the project are also to be deleted.

Furthermore, the change actions, rules, and associated timinginformation can be queried directly from the reminder index. Informationrelated to the change actions (such as change action information, taskidentifiers, timing information, and associated rules) can betransferred to a requestor responsive to a query. In this way, pendingchanges can be directly discovered by an associated data system insteadof searching all data records to discover any pending changes attachedto any data records in the data records themselves. Furthermore,promoted properties, as discussed above, can be included in the datarecords indicated by the change actions. These promoted properties canbe employed during execution of the change actions to identify anyparent data records, associated data records, or related data records ordata record properties that might be affected by any rules or changeactions.

To further illustrate change reminders and reminder indexes inproject/task examples, FIG. 7 is presented. FIG. 7 illustrates changereminders in data records in an example. FIG. 7 illustrates remindersfor processing in eventually consistent systems, and can be implementedon any of the systems discussed herein, such as system 100 in FIG. 1,system 1000 in FIG. 10, or system 1201 in FIG. 12, although variationsare possible. FIG. 7 illustrates keeping an execution index in reminderindex 713, so that a higher level timer system can periodically scan itand perform follow-up tasks. FIG. 7 illustrates a process including: (1)a reminder is created, based on logic, to make a stateful evaluation atsome later specified time, and (2) a background process periodicallyqueries for and executes these reminders. Such reminders in step (1) canallow for simulating “cascading” edits at a scope larger than the scopeof transactional locking allowed for by the system, and may also allowfor deferring edits later in time.

Operation 700 illustrates an action of moving task-A to recycle bin 712that includes initially taking a change lock on the task. The changelock is included in transaction locks 710 during the placement of task-Ainto recycle bin 712 and establishment of a reminder in reminder index713. Once task-A is placed into recycle bin 712 and the reminder inreminder index 713, the change lock for task-A can be released to allowintervening edits from other processes and users while task-A is in therecycle bin waiting for deferred purging. This example system has atiming rule that, after a predetermined number of days of having atarget data record (i.e. task-A) in the recycle bin, a further datarecord that references the target data record should also be deleted(i.e. project-A). In this example, according to logical rules includedin reminder index 713 for task-A, project-A (which contains task-A) isto be deleted after a predetermined amount of time that task-A is toremain in the recycle bin. System rules 720 indicate example logicalrules for evaluating reminder operations related to task-A in recyclebin 712. Specifically, system rules 720 indicate that a data record(task-A) should be purged from the recycle bin after 30 days in therecycle bin, and any corresponding project that contains that taskshould be deleted. The system rules can be configured as global,per-record type (i.e. all tasks have similar rules), or specific to eachrecord, including combinations thereof.

In operation 701, task-A is shown in recycle bin 712. Reminder index 713also includes a reminder to check task-A again at a later time ‘X’.After the configured amount of time passes for the reminder, thereminder process executes the reminder for task-A. FIG. 7 illustratesoperation 702 with reminder process 714 periodically ‘pulling’ orchecking reminders in reminder index 713 to determine if the timethreshold of the rule is met, i.e. if 30 days have passed. FIG. 7further illustrates operation 702 with reminder process 714 executingthe associated rule by establishing a lock for the project associatedwith the task (project-A) and deleting project-A. Intervening edits mayhave been made to task-A or project-A because reminder process 714 orany associated deletion process did not hold onto the change locks forthe task or project during any intervening time. Reminder process 714can automatically resolve those intervening edits to ensure properaction is taken for the affected data records (i.e. deleting properproject data record and task data record). Intervening edits can includechanges to project names, task names, or other various properties of theassociated data records for projects and tasks which occurred whentask-A waits for purging from recycle bin 712. In alternate examples, achange lock is held on task-A while task-A is in the recycle bin anduntil execution of the reminder for task-A. In these alternativeexamples, intervening changes to task-A are not allowed due to thetransaction lock.

Turning now to FIGS. 8-11, enhanced data and change replication isdiscussed, among other features. Many data handling platforms andsystems, such as those discussed herein, include more than oneassociated data center. In FIG. 1, multiple data centers are shown, andthese can be located remotely or locally to each other. In someexamples, the data centers comprise ‘cloud’ or distributed computingsystems. However, difficulties can arise when replicating data changesacross many data centers that handle sets of redundant data. In exampleswhere all replicated edits are strongly ordered, processing might bedifficult to parallelize. However, in the examples below, by giving upsome measure of strong ordering, a system can achieve an enhanced levelof parallelism. Advantageously, this idea improves the performance ofasynchronous replication in data storage systems.

Data for a service can be stored in one data center, called the primary,and replicated to one or more other data centers (the secondary,tertiary, and so on). Changes and edits are typically received at theprimary data center from users. Replication can be synchronous, whereevery edit is written to every replica before the edit is acknowledgedto a user. Replication can instead be asynchronous, where an edit isacknowledged when it is received from a user by the primary, and onlylater written to the secondary.

Asynchronous replication needs to be ordered if data is to remainconsistent on the replicas. For example, suppose that a document storagesystem allows changing the title of a document. A user edits a documentwith two updates: (A) set the title to “Revenue”; and (B) set the titleto “Profits.” The final effect is that the document title is set to“Profits.” If these edits are then replicated to a secondary data centerin an incorrect order, the edits will be run in this order: (B) set thetitle to “Profits”; and (A) set the title to “Revenue.” If this happens,the title of the document will be different on the secondary and theprimary, which is an error.

One way to handle the replication is to fully order updates. Forexample, every edit to every document in the storage system is given asequence number, and the updates are run in the exact same order on thereplica. Since all of the edits are strictly sequenced, none of them canbe run in parallel. This can become a bottleneck if data center loadincreases. The examples herein trade some of the ordering forparallelism. Edits are grouped into “feeds” which are commutative withrespect to each other, and edits are only guaranteed a relative order ofexecution on a per-feed basis. Commutativity refers to having updatesfrom different feeds having the same outcome regardless of which isexecuted first, even if the updates may be for items logically relatedto each other. The quantity of feeds can be tuned to increaseparallelism, which can improve performance, or tuned to increase thestrength of ordering.

For example, in a system with tasks stored in projects, a task and itscontaining project may be in separate feeds. An update in the first feedmay create the project, and the update in the second feed may create thetask. At first glance, the task creation might not seem commutative withthe project creation, because the project is the container for the task.Instead, by using a placeholder project if the task happens to becreated first, and filling in the project with the correct details whenit is fully created, the task can be created “out of order” such thatthe result is the same no matter which creation executed first. Sincethe outcome is the same regardless of which creation executes first, thetwo creations (project and task) are considered commutative.

There are many different ways to organize document/data edits intofeeds. A first example structure is to divide documents which do notinteract with each other into feeds based on hashing an identifier ofthe document. For example, a document storage subsystem can includeedits to two documents that never interact with each other, i.e. thedocuments are independent. Suppose that in the primary data center,these edits are received:

-   -   Edit1) UserA edits Document1, setting the title to “Profits”    -   Edit2) UserB edits Document2, setting the title to “Morale”    -   Edit3) UserA edits Document1, setting the title to “Revenue”    -   Edit4) UserB edits Document2, setting the title to “Events.”

To have the same results on the secondary (replica) data center, Edit1must be executed before Edit3, and Edit2 must be executed before Edit4.But the relative orderings of edits to Document1 and to Document2 do notmatter. Thus, the edits can be divided into feeds according to theassociated documents, based on their identifiers:

Feed 1:

-   -   Edit1) UserA edits Document1, setting the title to “Profits”.    -   Edit3) UserA edits Document1, setting the title to “Revenue”.

Feed 2:

-   -   Edit2) UserB edits Document2, setting the title to “Morale”.    -   Edit4) UserB edits Document2, setting the title to “Events”.

The two feeds can now be executed in parallel on the secondary datacenter, and associated throughput is doubled.

Further example feed algorithms can group related documents together.For example, if editing a document should cause a cascading update to asubdocument, an implementation could mandate that the edit and thecascading update be in the same feed. Similarly, such as in examplesthat include tasks and projects, a “containment” or structuralrelationship can be modeled as commutative updates by using placeholdercontainers, allowing placeholder containers and contained objects to bein distinct feeds.

Various improvements to data verification, data set coherency checking,and data center management technology may be appreciated from theforegoing implementations. The ability of data center management systemsto handle data verification efficiently using parallel feeds providesfor enhanced operation of any associated data records, databases, anduser interaction. Technical effects improve the functioning of computerand computer systems which might include databases, applications (suchas project management applications), and data storage systems. Thisprovides various technical effects and advantages by increasing theresponse time of data storage systems and reducing processor load andmore efficient code execution for associated applications.

Turning now to the operations of FIG. 8, operations 800-803 illustratereplication ordering for data in an example. FIG. 8 illustratesreplication ordering in a cross-document eventual consistency system. Inthe examples shown in FIG. 8, document edits or data edits can begrouped into feeds, where operations in different feeds are fullycommutative against each other. Feeds can be processed in parallel,granting performance increases without impacting data correctness. Thenumber of feeds and the strength of ordering for edits are a scale onwhich optimizations can be made in each implementation of this idea.

FIG. 8 illustrates a method of managing changes among redundant datastorage spaces. The method includes, across a plurality of data centers,redundantly storing sets of structured data records, with fields of thestructured data records correlated among the sets by one or morerelational associations. The method includes, in a first data center,receiving change actions related to the structured data records andselectively placing ones of the change actions into a plurality ofchange feeds, where the change feeds can be executed in parallel toimplement the change actions. The method also includes propagating thechange actions to ones of the data centers as operations ordered withineach of the change feeds, wherein each of the data centers implement thechange actions in the change feeds in parallel to affect the structureddata records stored by the associated data center.

In operation 800, two documents are first in an original state, namelydocument 1 “profits” and document 2 “morale.” These documents arereplicated from primary 810 to secondary 811, as shown by thereplication pipeline in operation 800 of FIG. 8. The documents are thenrenamed in operation 801, such as responsive to user commands to editthe names in the 1^(st) and 2^(nd) edits. Primary data center 810 canplace change actions related to the edits in one or more commutativechange feeds for implementation of the change actions in parallel inprimary data center 810. Further details regarding the change feeds arediscussed in operation 803 below. Once the change actions areimplemented, primary data center 810 transfers replication data to oneor more redundant data centers, such as secondary data center 811. Thisreplication data can include indications of the change actions, edits,or other information for altering the structured data records. Thechange actions can be indicated in the replication data as being withinone or more change feeds. Additionally, this replication data caninclude checksum or hash data which is used to verify completion of thechange actions in the redundant data centers. The hash data compared toa hash generated on a data record (or portion thereof) afterimplementation of the accompanying change actions. If a mismatch occurs,then the change actions might not have been performed correctly. Afurther discussion of this hash comparison is found in FIG. 9 below.Primary data center 810 can generate this hash data related the state ofthe structured data records after implementing the change records.Further examples of the hash data are included in FIG. 9.

Operation 802 shows a traditional system where all edits are stronglyordered, creating a bottleneck. Specifically, edit 820 and edit 821 arepropagated to secondary 811 in a sequential order. This order istypically the order in which the edits are received by primary 810, andthus edits 820 and 821 are replicated to secondary 811 as seen in FIG.8. Operation 803 illustrates an enhanced operation using feeds.Advantageously, in operation 803, edits are bucketed such that edits ineach bucket are commutative, and can be run in parallel. Commutativefeeds can execute in parallel with each other so that feeds can beexecuted in any order as compared to other feeds, but change actionswithin a particular feed are executed in order. In this example,operations renaming distinct documents are commutative, and edits 820and 821 occur in parallel. Replication data transferred by primary datacenter 810 indicates edits 820 and 821 to secondary data center 811, andthis replication data indicates the edits as being within one or morecommutative feeds, such as shown in FIG. 8.

FIG. 9 illustrates verification of data changes in an example, which canbe included along with commutative feeds as seen in FIG. 8. FIG. 9illustrates immediate and eventual verifications in multi-documentreplication. FIG. 9 also illustrates a method of managing coherencyamong partitioned data storage spaces. The method includes storing setsof structured data records among the partitioned data storage spaces,with fields of the structured data records correlated among the sets byone or more relational associations, receiving change actions related tothe structured data records and selectively placing ones of the changeactions into a plurality of change feeds, where the change feeds can becommutatively executed in parallel to implement the change actions. Themethod also includes, while implementing the change actions inassociated change feeds, performing consistency verifications of firstones of the change actions upon completion of each of the first ones ofthe change actions, and selectively delaying consistency verificationsof second ones of the change actions until at least subsequent changeactions are performed that affect similar structured data records. Insome examples, the delayed consistency verifications can be optionallyignored once selectively delayed.

Turning now to the operations of FIG. 9, operation 900 shows a project(project-A) and a contained task (task-A) in an original state inprimary data center 910. This original state can be established by auser to create a project and add one or more tasks to that project.Project-A and associated task-A are then replicated to secondary datacenter 911. In operation 901, the project and task are renamed, such asresponsive to user input received at primary data center 910. Similar tothe operations of FIG. 8, the renaming edits can be placed intocommutative feeds in primary data center 910, implemented in primarydata center 910, and replicated to secondary data center 911. Theproject and task edits are replicated to secondary data center 911, asseen in operation 902 for project replication data 920 and taskreplication data 921. The replication of the two associated edits920/921 (project and task names) to secondary data center 911 is done inparallel, such as in commutative feeds established by primary datacenter 910. As seen in operation 902, the task renaming may replicate tosecondary data center 911 before its containing project renamingreplicates.

Task replication data 921 expanded in detail in FIG. 9. This taskreplication data can be representative of the replication data discussedfor projects and replication data discussed in FIG. 8, althoughvariations are possible. For example, the hash data might be omitted insome examples. In this example, task replication data 921 includes adescription of the changes associated with renaming the task, such as anew task name, which is indicated in change task properties 922. Changetask properties 922 can also indicate associated change feeds orcommutative feeds into which changes are arranged. Task replication data921 also includes one or more hashes, namely “hash of all taskproperties 923” and “hash of all parent project properties 924.” Thesehashes comprise hash checksums in this example, and can be calculatedusing various hashing processes. In FIG. 9, all edits to a task arestrictly sequenced, and the task hash must match. The parent projecthash only needs to opportunistically match, because as shown above, itmay not be fully replicated when the task changes replicate.

A further discussion of the replication and verification process usinghashes follows. FIG. 9 illustrates “immediate” or “consistent”verifications that can be done immediately upon replication forproperties which depend only on the replication of a single document,and “eventual” verifications that are tried immediately uponreplication, but can be retried later if they may be relying on adocument which has not replicated yet. This example illustrates the twokind of hashes that can be sent along with replicated records, namelyhashes 923 and 924. Consistent/immediate hashes verify properties homedon or intrinsic to a data record, which are immediately verified forcorrectness/consistency. Eventual hashes can include promoted propertiesincluded in a data record, and verification may first need other records(which may be replicated in different feeds) to be processed before thehashes will match. In this example, hash 923 comprises aconsistent/immediate hash type, and hash 924 comprises an eventual hashtype.

Large distributed or cloud data services often have a single datacenter, called the primary. Collections of data (here called “documents)are then replicated to other data centers, like a secondary, a tertiary,and so on. This allows for traffic to “fail over” to another data centerif the primary is unresponsive. The two major styles of replication aresynchronous (in which edits are written to all replicas before they areacknowledged) and asynchronous (in which edits are acknowledged andlater replicated). Asynchronous systems are often used because they arefaster and more fault tolerant. Synchronous systems have to wait foracknowledgement of edits on multiple replicas, and cannot be successfulif the majority of replicas are not responsive. But one of the maindisadvantages of asynchronous systems is that replication is difficultto implement correctly. It is possible for several categories of bugs toresult in document data loss or corruption when asynchronouslyreplicating documents. This is true regardless of whether updates aresent as “deltas” (changes) or as full documents on every edit. Theexamples in FIG. 9 show various ways to detect lost or corrupteddocuments using the hashing scheme and both immediate and eventualverifications.

In some systems, periodic checksums are sent of documents duringreplication, so that the replica can check if all of its documentsmatch. For example, the bytes of every field in every record can bemerged together into a single value via a hash function, and the hashescan be compared between data centers. However, this constrainsreplication to be run in a single “feed,” meaning that all replicatededits must be strongly ordered. Having edits strongly ordered means thatthey cannot be run in parallel, reducing throughput. Advantageously, inthe examples herein, changes to documents are distributed acrossmultiple feeds in such a way that their edits do not interact with eachother. However, this can lead to edits for multiple documents which canoccur in a different order in the primary data center and in thesecondary data center.

If edits happen in a different order in the secondary data center, andthen a checksum or hash is evaluated, the intermediate state in thesecondary data center may not match the intermediate state of theprimary data center. This is especially relevant for “promotedproperties” discussed herein, which comprise properties or attributes ofone document that are available in another document, reflectingproperties that are reflected across a foreign-key association. Forexample, in a task management system, the title of a project may be“promoted” to a task contained by that project, so that the promotedproperty is available when reading the task, and the promoted propertyis available across the foreign-key association of containment. Thefinal state of the task is that the task has been created and also hasthe project title promoted to a field of the task. However,replication/creation of the task and the creation of the project canhappen in either order on the secondary data center.

Further, single-feed approaches typically have each data centerperiodically “frozen,” and not accepting edits while checksums arecomputed. The frozen state indicates that the primary data center stopsaccepting edits for a duration sufficient to compute a checksum, and thesecondary data center needs to process the same edits and pauseprocessing a duration sufficient to compute and compare the checksum. Anadvantage of the examples herein using multiple feeds lies in ‘when’ tocheck properties of replicated objects, in part because although editsacross multiple documents are not strongly ordered, edits to a singledocument are strongly ordered. The examples herein make a distinctionbetween “Immediate” or “Consistent” verifications, and “Eventual”verifications Immediate verifications are verifications of fields thatcan only depend on a single object, and can be evaluated every time therelevant objects are replicated.

Consider, for example, an example system, such as found in FIG. 9, withprojects that contain tasks, where both the tasks and projects havenames. However, in this example, the task object has at least two fieldsthat are returned when a task is read: the task name (an intrinsicfield), and the project name (a promoted property field, indicated by‘PP-x’ in FIG. 9). The project name is promoted to the task (i.e. aproperty of the project, not of the task). In operation 900, task-Ashows a data field with a promoted property comprising the name of aproject that includes task-A, namely “PP-A” which represents the name ofthe project (project-A). An edit is made to the task name and theproject name, from “-A” to “-B,” and the associated data recordproperties are changed. Specifically, project-A is renamed to project-B,where the project name is an intrinsic property or intrinsic data fieldof the project. Task-A is also renamed, to task-B, and the task name isan intrinsic property or intrinsic data field of the task. The promotedproperty included in the task which reflects a property of the projectdata record should also be updated to reflect the edit to the projectname, and thus operation 901 shows “PP-B” resulting in task-B.

In FIG. 9, redundant data centers are employed, and any changes receivedat primary data center 910 are replicated to secondary data center 911.When an edit to the task is replicated to a secondary data center, thetask name can be verified to ensure the replication of the edit iscorrect in the secondary data center. This is an example of an Immediateverification: the values of all immediately-verifiable properties arehashed together whenever an associated edit is performed on the primarydata center, and when the edit is later executed on the secondary datacenter, the hash is re-computed. If the two hashes do not match, thereis an error, and corrective action may be taken (such as alerting anerror to a user) or waiting predetermined amount of time an attemptingan associated hash comparison again. If the two hashes match, there isno error and the change/edit was replicated corrected to the secondarydata center.

An Eventual verification is a verification of a field whose valuesdepend on other objects. In the examples of project/tasks discussedherein, the project name as promoted to the task (i.e. “PP-x”) is anexample of a field in the task that depends on another object andEventual verification can be performed on changes/edits to the projectname (as contained in the field of the associated task). When the taskand the project are replicated independently, such as in differentfeeds, uncertainty can arise whether the edited properties will matchwhen the edit to the task is evaluated on both the primary data centerand the secondary data center.

Consider this sequence for a primary data center:

-   -   The Project is created, with name ProjectA.    -   The Task is created, with name Task1, and has the promoted        project title ProjectA included. The Immediate verification hash        is performed just with the value Task1, and the Eventual        verification hash is performed just with the value ProjectA.    -   The Project is renamed to ProjectB.        On the secondary data center, the sequence might replicate like        this:    -   The Project is created, with name ProjectA.    -   The Task is created, with name Task1, and has the promoted        project title ProjectA. The Immediate verification hash is        performed just with the value Task1, and the Eventual        verification hash is performed just with the value ProjectA.    -   The Project is renamed to ProjectB.

In the sequences above, the Immediate and Eventual hashes will match onboth data centers. However, the project and the task can replicate in adifferent order with respect to each other, and on the secondary datacenter the sequence might instead replicate like this:

-   -   The Project is created, with name ProjectA.    -   The Project is renamed to ProjectB.    -   The Task is created, with name Task1, and has the promoted        project title ProjectB. The Immediate verification hash is        performed just with the value Task1, and the Eventual        verification hash is performed just with the value ProjectB.

In the case above, the rename of the project has been swapped with thetask creation. The result is that the Immediate hash still matches forthe task names, but the eventual hash does not initially match for theproject names. The Immediate hash (which must never fail, else report anerror) has been made distinct from the Eventual hash (which mayinitially fail for legitimate reasons, but pass at a later time). Insome cases, Eventual hash failures can be ignored and Immediate hashfailures lead to an error. In other cases, failing Eventual hashes canbe handled by any of the following: (1) watching for further replicatededits which may resolve the mismatch, (2) raising an alert if themismatch is not resolved after a specified amount of time, or (3)resetting a timer each time an edit is seen for a document or a documentin a promoted-properties relationship changes. Stating (3) another way,an alert can be raised if the data is still inconsistent and the amountof time since an edit was seen exceeds the expected replication delay ofthe system. To resolve Eventual hash failures, the secondary data centercan check with the primary data center to directly to resolve themismatch, request a resend of the associated replication data or hashes,or collect associated statistics to watch for trends that may indicateissues with replication, including combinations thereof.

Thus, the examples above discuss various improvements to dataverification, data set coherency checking, and data center managementtechnology which may be appreciated from the foregoing implementations.The ability of data center management systems to handle dataverification efficiently using parallel feeds with immediate anddeferred verification provides for enhanced operation of any associateddata records, databases, and user interaction. Technical effects improvethe functioning of computer and computer systems which might includedatabases, applications (such as project management applications), anddata storage systems. This provides various technical effects andadvantages by increasing the response time of data storage systems andreducing processor load and more efficient code execution for associatedapplications.

As mentioned above, large distributed data services often have a primarydata center. Collections of data (herein called “documents”) are thenreplicated to other data centers, like a secondary, a tertiary, and soon. This allows for data center traffic to “fail over” to another datacenter if the primary is destroyed. FIGS. 10 and 11 illustrate exampledata storage environments for replication control via topology state.

FIG. 10 illustrates system 1000. System 1000 includes three data centers1010, 1020, and 1030, as well as topology control node 1040. These datacenters can include similar elements as discussed in FIG. 1, althoughvariations are possible. For example, data center 1010 includes logic1011 and data storage 1012, data center 1020 includes logic 1021 anddata storage 1022, and data center 1030 includes logic 1031 and datastorage 1032. Each of the data centers in FIG. 10 are communicativelycoupled by one or more network links which can be coupled over one ormore packet networks. In some examples, the data centers are located ingeographic locations remote from each other and coupled via the Internetor other distributed networks. Each of the data centers in FIG. 10provide redundant storage of data records in associated data storageelements. Initially, FIG. 10 shows a first data center (1010) designatedas a primary data center, a second data center (1020) designated as asecondary data center, and a third data center (1030) designated as atertiary data center. User input and user interaction is handled by theprimary data center, as routed by topology control node 1040. As will bediscussed below, enhanced operation is provided to change statusdesignations among the data centers.

An example of operation of FIG. 10 can include a method of managingredundant data storage centers. The method includes redundantly storingdata records across the redundant data storage centers, with a first ofthe data storage centers operating as a primary data storage centerconfigured to respond to at least user input related to the datarecords. Responsive to designating a second of the data storage centersas the primary data storage center, the method includes placing thefirst of the data storage centers into an intermediate mode which ceasesresponse to the user input by the first of the data storage centers, andin the first and the second of the data storage centers, executingpending operations related to previous user input received by the firstof the data storage centers. The method also includes designating thesecond of the data storage centers as the primary data storage centerconfigured to respond to at least further user input related to the datarecords. In some examples, designating the second of the data storagecenters as the primary data storage center comprises determining aquorum among each of the data storage centers to designate the second ofthe data storage centers as the primary data storage center. When thefirst of the data storage centers is in the intermediate mode, the firstof the data storage centers can operate in a read-only mode. In furtherexamples, once the first of the data storage centers completes all ofthe pending operations, the first of the data storage centers can beplaced into an out-of-service mode.

FIG. 11 illustrates example processes for changing states among datacenters, and the operations of FIG. 11 can also be applied to theelements of FIG. 10. In operation 1100, data centers 1110 and 1120 arein an original state with an original primary data center 1110 andoriginal secondary data center 1120. Further data centers can beincluded, but are omitted in FIG. 11 for clarity. Topology state 1111indicates that data center 1110 is set to primary and topology state1121 indicates that data center 1121 is set to secondary. Backgroundprocesses 1112, 1113, 1122, 1123 each handle various functions of theassociated data center, such as receiving and implementingchanges/edits, replicating data, checking hashes, issuing alerts/errors,among other functions. Primary data center 1110 receives user traffic,such as edits, changes, additions, deletions, queries, or other dataoperations. Replicated data 1130 is generally transferred from primarydata center 1110 to secondary data center 1120, or other data centers,responsive to changes made to data managed in primary data center 1110.

In operation 1101, the process of changing which data center is theprimary is initiated. This process can be initiated responsive tooutages, problems, or unresponsiveness detected for the current primarydata center, responsive to instructions by a control node oradministrator to initiate the process in anticipation of an outage ormaintenance of the primary data center, or periodically after theprimary data center has been a primary for a predetermined quantity oftime, among other initiation triggers. Original primary data center 1110is first set to an intermediate state which waits for backgroundprocesses 1112-1113 to exit and for associated data to flush, such asreplicated data 1131 to be replicated to data center 1120. Topologystate 1111 indicates that data center 1110 is set to intermediate andtopology state 1121 indicates that data center 1121 is set to secondary.

In operation 1102, another data center 1120 is set as the primary sothat data center 1120 starts accepting user traffic. Topology state 1111indicates that data center 1110 is set to intermediate and topologystate 1121 indicates that data center 1121 is set to primary. Datacenter 1102 can begin accepting user traffic and implementing dataprocesses responsive to the user traffic. Changes/edits to the data canbe further replicated from data center 1120 to other data centers notshown in FIG. 11. In operation 1103, the old primary data center 1110 isset to an out of service state in topology state 1111, and data center1120 remains in the primary state. Once maintenance or repairs arecomplete to data center 1110, the primary designation can be changedback to data center 1110 or remain with data center 1120. The out ofservice state prevents data center 1110 from receiving user input fromusers and any replication data from the current primary data storagecenter.

In FIG. 11, background processes and other state-specific actions, likeaccepting user traffic, can be controlled by a single topology controlnode. Unplanned failover is achieved using extension of the process inFIG. 11 instead of an extensive special case. For example, loss ofcontrol of a primary data center can be handled with a quorum system toestablish the data center state. Specifically, remaining data centerscan ‘vote’ to establish that a primary data center has becomeunresponsive or entered into an inactive state. A new primary datacenter can be selected among the remaining data centers based on apredetermined ordering or other factors, such as performance, capacity,or latency factors.

Advantageously, the processes in FIGS. 10 and 11 efficiently manage thebackground operations of a replicating distributed data service. Areplicating distributed data service comprises a data service thatstores data in one data center (the primary) and replicates the data toanother data center (the secondary) and optionally replicates the datato others (like a tertiary). If the primary data center is disabled,traffic can be routed to the secondary data center (referred to as“failing over” or “performing a failover”).

Topology refers to the set of datacenters and associated states,including which datacenter is a primary, secondary, and the like. Thus,topology includes information indicating which data center is currentlythe primary and which is the secondary. Data centers can be largelysimilar in terms of hardware and software composition, but one has beendesignated as the primary data center in the topology control node. Eachrole may have different associated jobs that need to be run. Forexample, the primary data center needs to export replication data, andthe secondary data center needs to import replication data. Keepingthese jobs in sync with the topology control node is important. Forexample, an error can arise to make a data center a primary but not runall of the jobs required of a primary data center. Similarly, onlyprimary data centers typically can accept user traffic.

Some data center control schemes define steps for each possible statechange from a Start state to a Finish state, such as: (A) Set the stateto an intermediate state; (B) Stop each job which is run on the Startstate; (C) Start each job which is run on the Finish state; (D) Set thestate to Finish. Other data center topologies are more complicated fortransitions which affect multiple data centers, like a failover.Consider the example steps from some data center topologies to move theOriginal data center from Primary to Out Of Service, and the New datacenter from Secondary to Primary: (A) Set the state of Original toIntermediate; (B) Stop each job on Original which pertains to Primary(there may be many jobs, so these could have many substeps); (C) Ensuredata has flushed from Original to New; (D) Start each job on New whichpertains to Primary; (E) Start each job on Original which pertains toOut Of Service; (F) Set Original to Out Of Service; (G) Set New toPrimary.

The procedures described in the previous paragraph can be error-prone,in part because errors arise when two data centers run Primary jobs atthe same time (or both accept user traffic). Also, the approachdescribed in the previous paragraph has many explicit steps, which mightbe coded incorrectly. Downtime is minimized when one data center isrunning Primary jobs for as much of the time as possible. Further, animportant aspect of state transitions for data centers is that theyshould be resilient to error, such as having the ability to bere-triable. The procedures described in the previous paragraph aretypically special-cased (to flip or not flip many differentcontrol/network switches or topology control node settings) when thesystem is not working properly. When many switches are flipped one at atime, some processes might spend more time turned off than if controlledby a master switch. One such issue is the case of “unplanned failover,”meaning that an associated replication pipeline is not flushingcompletely. When every subsystem is called independently to flush data,handling operations for one of the subsystems can be difficult to managecorrectly. Another difficulty of the procedures described in theprevious paragraph is the “loss of control” scenario, where a topologycontrol node is unable to contact the primary data center to set itsstate. In an approach with many subsystems that have to have their statechanged explicitly, the operational procedures might not account for oneof them being unreachable.

An advantageous aspect of the examples herein is that all jobs arecontinuously evaluated by the job scheduler to see whether they shouldbe running on the topology. A straightforward mapping from each job tothe topology states in which it should be running is established, wherethe steps of starting and stopping jobs do not need to be encoded intodata center state changes. Transitions which affect multiple datacenters, like a failover, can also be efficiently handled. For example,a failover process to move an original Primary data center from Primaryto Out of Service, and a new data center from Secondary to Primary is asfollows:

-   -   (1) Set the state of the original Primary data center to        Intermediate    -   (2) Wait for each job on the original Primary data center which        pertains to the Primary data center to finish executing    -   (3) Ensure replication data has flushed from the original        primary data center to the new primary data center    -   (4) Set the new data center to be the Primary data center    -   (5) Set the original Primary data center to Out of Service

This preceding example has fewer steps than in other topologies andcontrol schemes, in part because jobs do not need to be explicitlystarted for the new state as the job manager of the associated datacenters will handle that. Also, stopping jobs merely includes waitingfor those jobs to finish, so there are fewer state transitions andsubsteps which can complicate retry. This enhanced scheme also allowsfor a very simple mechanism for unplanned failover by having step (1)above simply omitted from the workflow. Different subsystems do not haveto be accounted for, only a single settings switch is needed in thefailover operation. Finally, this enhanced scheme allows for loss ofcontrol scenarios by omitting steps (1), (2), and (3) above. This allowsthe system to establish a new primary while an original primary isunresponsive.

However, the original primary should still be set to out of service (sothat the original primary does not accept user traffic), and a furthermechanism is included. This can be achieved by making a quorum system ofthe data centers (using three or more data centers in the topology).When a first data center believes it is the primary, the first datacenter periodically checks with other data centers in an associatedtopology to ensure that they agree. If more than half of the datacenters agree that the first data center is primary, the first datacenter can continue acting as primary Otherwise, if more than half ofthe data centers think that the first data center is not primary orcannot be reached to give a vote, the first data center willautomatically stop accepting user traffic. This also allows an operatorto force the first data center to be out of rotation/use in a topology,even if the operator cannot reach the first data center, as long as theoperator can reach more than half of the other data centers to informthem of the change. This greatly increases the resilience of the system.

One example quorum is shown in FIG. 10. FIG. 10 includes quorum 1050which includes a vote from each of the data centers as to which datacenter is currently set as the primary data center. Two data centerscurrently indicate that data center 1010 is the primary and one datacenter currently indicates that data center 1020 is the primary. Thus,topology control node and the associated data centers operate accordingto data center 1010 as primary. If data center 1010 became unresponsive,then it would not provide a ‘vote’ and another data center could takeover as primary. Likewise, topology control node 1040 can control whichdata center is the primary by reporting to each data center the selectedprimary designation. If one of the data centers fails to receive thisdesignation initially, then the remaining data centers still canindicate the correct primary due to the quorum process. Each data centercan exchange quorum data indicating current primary designations withthe other data centers, and can receive topology state changes fromtopology control node 1040.

In these quorum examples, each data center will periodically check thequorum, such as by receiving quorum data from the other data centers orrequesting the quorum data from the other data centers. If a currentlynon-primary data center, such as a secondary data center, receivesquorum data that indicates the secondary data center is now a primarydata center, then the secondary data center can change a correspondingstate to primary from secondary and being to receive user traffic andreplicate changes to other data centers. The quorum data can indicate acurrent ‘vote’ from each data center as to which data center is theprimary, among other designations or states. The quorum data canindicate an identifier of the primary data center, such as a networkidentifier of a data center, unique identifier for a data center, orother designation. The quorum data can also include more than onedesignation, with a ranking of possible primary data centers.

Various improvements to replication control and data center managementtechnology may be appreciated from the foregoing implementations. Theability of data center management systems to handle failover and rolechanges efficiently provide for enhanced operation of any associateddata records, databases, and user interaction. Technical effects improvethe functioning of computer and computer systems which might includedatabases, applications (such as project management applications), anddata storage systems. This provides various technical effects andadvantages by increasing the response time of data storage systems andreducing processor load and more efficient code execution for associatedapplications.

FIG. 12 illustrates computing system 1201 that is representative of anysystem or collection of systems in which the various operationalarchitectures, scenarios, and processes disclosed herein may beimplemented. Examples of computing system 1201 include, but are notlimited to, server computers, rack servers, web servers, cloud computingplatforms, and data center equipment, as well as any other type ofphysical or virtualized server machine, and any variation or combinationthereof. Computing system 1201 can be representative of elements of datacenters 110-112 of FIG. 1, or data centers 1010, 1020, and 1030 of FIG.10, although variations are possible.

Computing system 1201 may be implemented as a single apparatus, system,or device or may be implemented in a distributed manner as multipleapparatuses, systems, or devices. Computing system 1201 includes, but isnot limited to, processing system 1202, storage system 1203, software1205, communication interface system 1207, and user interface system1208. Processing system 1202 is operatively coupled with storage system1203, communication interface system 1207, and user interface system1208.

Processing system 1202 loads and executes software 1205 from storagesystem 1203. Software 1205 includes structured data handling environment1206, which is representative of the processes discussed with respect tothe preceding Figures. When executed by processing system 1202 toenhance data record processing and data center handling, software 1205directs processing system 1202 to operate as described herein for atleast the various processes, operational scenarios, and sequencesdiscussed in the foregoing implementations. Computing system 1201 mayoptionally include additional devices, features, or functionality notdiscussed for purposes of brevity.

Referring still to FIG. 12, processing system 1202 may comprise amicro-processor and processing circuitry that retrieves and executessoftware 1205 from storage system 1203. Processing system 1202 may beimplemented within a single processing device, but may also bedistributed across multiple processing devices or sub-systems thatcooperate in executing program instructions. Examples of processingsystem 1202 include general purpose central processing units,application specific processors, and logic devices, as well as any othertype of processing device, combinations, or variations thereof.

Storage system 1203 may comprise any computer readable storage mediareadable by processing system 1202 and capable of storing software 1205.Storage system 1203 may include volatile and nonvolatile, removable andnon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, or other data. Examples of storage media include randomaccess memory, read only memory, magnetic disks, optical disks, flashmemory, virtual memory and non-virtual memory, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other suitable storage media. In no case is the computer readablestorage media a propagated signal.

In addition to computer readable storage media, in some implementationsstorage system 1203 may also include computer readable communicationmedia over which at least some of software 1205 may be communicatedinternally or externally. Storage system 1203 may be implemented as asingle storage device, but may also be implemented across multiplestorage devices or sub-systems co-located or distributed relative toeach other. Storage system 1203 may comprise additional elements, suchas a controller, capable of communicating with processing system 1202 orpossibly other systems.

Software 1205 may be implemented in program instructions and among otherfunctions may, when executed by processing system 1202, directprocessing system 1202 to operate as described with respect to thevarious operational scenarios, sequences, and processes illustratedherein. For example, software 1205 may include program instructions forimplementing enhanced data record processing and handling for datacenter operations.

In particular, the program instructions may include various componentsor modules that cooperate or otherwise interact to carry out the variousprocesses and operational scenarios described herein. The variouscomponents or modules may be embodied in compiled or interpretedinstructions, or in some other variation or combination of instructions.The various components or modules may be executed in a synchronous orasynchronous manner, serially or in parallel, in a single threadedenvironment or multi-threaded, or in accordance with any other suitableexecution paradigm, variation, or combination thereof. Software 1205 mayinclude additional processes, programs, or components, such as operatingsystem software or other application software, in addition to or thatinclude environment 1206. Software 1205 may also comprise firmware orsome other form of machine-readable processing instructions executableby processing system 1202.

In general, software 1205 may, when loaded into processing system 1202and executed, transform a suitable apparatus, system, or device (ofwhich computing system 1201 is representative) overall from ageneral-purpose computing system into a special-purpose computing systemcustomized to facilitate enhanced data record processing and data centerhandling. Indeed, encoding software 1205 on storage system 1203 maytransform the physical structure of storage system 1203. The specifictransformation of the physical structure may depend on various factorsin different implementations of this description. Examples of suchfactors may include, but are not limited to, the technology used toimplement the storage media of storage system 1203 and whether thecomputer-storage media are characterized as primary or secondarystorage, as well as other factors.

For example, if the computer readable storage media are implemented assemiconductor-based memory, software 1205 may transform the physicalstate of the semiconductor memory when the program instructions areencoded therein, such as by transforming the state of transistors,capacitors, or other discrete circuit elements constituting thesemiconductor memory. A similar transformation may occur with respect tomagnetic or optical media. Other transformations of physical media arepossible without departing from the scope of the present description,with the foregoing examples provided only to facilitate the presentdiscussion.

Structured data handling environment 1206 includes one or more softwareelements, such as OS 1221, applications 1222, and data control logic1223. These elements can describe various portions of computing system1201 with which a user interacts or with which structured data recordsare managed over one or more data centers. For example, OS 1221 canprovide software platforms on which user applications are executed andallow for receipt and handling of data records, changes to data records,and queries to sets of data records. User applications 1222 can be anyapplication discussed herein, such as project management, planning, taskmanagement, calendaring, or any other data record handling application,and can include user interface elements. Data control logic 1223provides for promoted properties in eventually consistent systems,reminders for processing in eventually consistent systems, replicationfeeds in a multi-document eventual consistency system, replicationordering in a cross-document eventual consistency system, immediate andeventual verifications in multi-document replication, and replicationcontrol via topology state, among other operations.

Communication interface system 1207 may include communicationconnections and devices that allow for communication with othercomputing systems (not shown) over communication networks (not shown).Examples of connections and devices that together allow for inter-systemcommunication may include network interface cards, antennas, poweramplifiers, RF circuitry, transceivers, and other communicationcircuitry. The connections and devices may communicate overcommunication media to exchange communications with other computingsystems or networks of systems, such as metal, glass, air, or any othersuitable communication media.

User interface system 1208 is optional and may include one or morenetwork interfaces for exchanging user input related to queries, datarecords, changes to data records, or other input. User interface system1208 can include other input devices and associated processing elementscapable of receiving user input from a user. User interface system 1208can provide output and receive input over a network interface, such ascommunication interface system 1207. User interface system 1208 may alsoinclude associated user interface software executable by processingsystem 1202 in support of the various user input and output devicesdiscussed above. Separately or in conjunction with each other and otherhardware and software elements, the user interface software and userinterface devices may support a graphical user interface, a natural userinterface, or any other type of user interface.

Communication between computing system 1201 and other computing systems(not shown), may occur over a communication network or networks and inaccordance with various communication protocols, combinations ofprotocols, or variations thereof. Examples include intranets, internets,the Internet, local area networks, wide area networks, wirelessnetworks, wired networks, virtual networks, software defined networks,data center buses, computing backplanes, or any other type of network,combination of network, or variation thereof. The aforementionedcommunication networks and protocols are well known and need not bediscussed at length here. However, some communication protocols that maybe used include, but are not limited to, the Internet protocol (IP,IPv4, IPv6, etc.), the transmission control protocol (TCP), and the userdatagram protocol (UDP), as well as any other suitable communicationprotocol, variation, or combination thereof.

In any of the aforementioned examples in which data, content, or anyother type of information is exchanged, the exchange of information mayoccur in accordance with any of a variety of protocols, including FTP(file transfer protocol), HTTP (hypertext transfer protocol), REST(representational state transfer), WebSocket, DOM (Document ObjectModel), HTML (hypertext markup language), CSS (cascading style sheets),HTML5, XML (extensible markup language), JavaScript, JSON (JavaScriptObject Notation), and AJAX (Asynchronous JavaScript and XML), as well asany other suitable protocol, variation, or combination thereof.

Certain inventive aspects may be appreciated from the foregoingdisclosure, of which the following are various examples.

Example 1

A method of operating a plurality of data centers that store structureddata records, the method comprising, across the plurality of datacenters, storing sets of the structured data records, with fields of thestructured data records correlated among the sets by one or morerelational associations. In a first data center that receives changeactions related to the structured data records, the method includesselectively placing the change actions into a plurality of change feeds,where the change feeds can be commutatively executed in parallel toimplement the change actions. In the first data center, the methodincludes implementing the change actions and propagating to at least asecond data center replication data comprising the change actionsindicated in the plurality of change feeds.

Example 2

The method of Example 1, further comprising, in the second data center,receiving the replication data comprising the change actions indicatedin the plurality of change feeds and implementing the change actions inparallel according to the change feeds to affect the structured datarecords in the second data center.

Example 3

The method of Examples 1-2, further comprising, while implementing thechange actions in associated change feeds, performing consistencyverifications of first ones of the change actions upon completion ofeach of the first ones of the change actions, and selectively delayingconsistency verifications of second ones of the change actions until atleast subsequent change actions are performed that affect relatedstructured data records.

Example 4

The method of Examples 1-3, further comprising, performing first hashcomparisons for intrinsic data fields of the structured data recordsaffected by the first ones of the change actions to perform theconsistency verifications of the first ones of the change actions, anddelaying second hash comparisons for promoted data fields of thestructured data records affected by the first ones of the changeactions.

Example 5

The method of Examples 1-4, where the promoted data fields comprise oneor more properties of other structured data records which reference thestructured data records affected by the first ones of the changeactions.

Example 6

The method of Examples 1-5, further comprising, delaying the second hashcomparisons for the promoted data fields of the structured data recordsaffected by the first ones of the change actions until after completionof the second ones of the change actions that affect related structureddata records.

Example 7

The method of Examples 1-6, where the replication data further comprisesfirst hash data used in the first hash comparisons and second hash dataused in the second hash comparisons.

Example 8

The method of Examples 1-7, further comprising, performing the secondhash comparisons, and if failures arise in the second hash comparisons,ignoring the failures, and re-performing the second hash comparisonsafter waiting a predetermined time.

Example 9

The method of Examples 1-8, further comprising, in the first datacenter, generating hash data related a state of the structured datarecords after implementing the change records in the first data center,and including the hash data in the replication data.

Example 10

An apparatus comprising one or more computer readable storage media, andprogram instructions stored on the one or more computer readable storagemedia. When executed by a processing system, the program instructionsdirect the processing system to at least store structured data records,with fields of the structured data records correlated among by one ormore relational associations. The program instructions direct theprocessing system to receive change actions related to the structureddata records, and selectively place the change actions into a pluralityof change feeds, where the change feeds can be commutatively executed inparallel to implement the change actions. The program instructionsdirect the processing system to implement the change actions andpropagate replication data comprising the change actions indicated inthe plurality of change feeds.

Example 11

The apparatus of Example 10, comprising further program instructions,when executed by the processing system, direct the processing system toat least implement the change actions according to the change feeds toaffect the structured data records.

Example 12

The apparatus of Examples 10-11, comprising further programinstructions, when executed by the processing system, direct theprocessing system to at least, while implementing the change actions inassociated change feeds, perform consistency verifications of first onesof the change actions upon completion of each of the first ones of thechange actions, and selectively delay consistency verifications ofsecond ones of the change actions until at least subsequent changeactions are performed that affect related structured data records.

Example 13

The apparatus of Examples 10-12, comprising further programinstructions, when executed by the processing system, direct theprocessing system to at least perform first hash comparisons forintrinsic data fields of the structured data records affected by thefirst ones of the change actions to perform the consistencyverifications of the first ones of the change actions, and delay secondhash comparisons for promoted data fields of the structured data recordsaffected by the first ones of the change actions.

Example 14

The method of Examples 10-13, where the promoted data fields compriseone or more properties of other structured data records which referencethe structured data records affected by the first ones of the changeactions.

Example 15

The apparatus of Examples 10-14, comprising further programinstructions, when executed by the processing system, direct theprocessing system to at least delay the second hash comparisons for thepromoted data fields of the structured data records affected by thefirst ones of the change actions until after completion of the secondones of the change actions that affect related structured data records.

Example 16

The method of Examples 10-15, where the replication data furthercomprises first hash data used in the first hash comparisons and secondhash data used in the second hash comparisons.

Example 17

The apparatus of Examples 10-16, comprising further programinstructions, when executed by the processing system, direct theprocessing system to at least perform the second hash comparisons, andif failures arise in the second hash comparisons, ignore the failures,and re-perform the second hash comparisons after waiting a predeterminedtime.

Example 18

A method of managing changes among redundant data storage spaces, themethod comprising across a plurality of data centers, redundantlystoring sets of structured data records, with fields of the structureddata records correlated among the sets by one or more relationalassociations, and receiving change actions related to the structureddata records and selectively placing ones of the change actions into aplurality of change feeds, where the change feeds can be commutativelyexecuted in parallel to implement the change actions. The methodincludes propagating the change actions to ones of the data centers asoperations ordered within each of the change feeds, where the ones ofthe data centers implement the change actions in the change feeds inparallel to affect the structured data records stored by the associateddata center.

Example 19

The method of Example 18, further comprising, while implementing thechange actions in associated change feeds, performing consistencyverifications of first ones of the change actions upon completion ofeach of the first ones of the change actions, and selectively delayingconsistency verifications of second ones of the change actions until atleast subsequent change actions are performed that affect similarstructured data records.

Example 20

The method of Examples 18-19, further comprising, performing first hashcomparisons for intrinsic data fields of the structured data recordsaffected by the first ones of the change actions to perform theconsistency verifications of the first ones of the change actions, anddelaying second hash comparisons for promoted data fields of thestructured data records affected by the first ones of the changeactions, where the promoted data fields comprise one or more propertiesof other structured data records which reference the structured datarecords affected by the first ones of the change actions.

The functional block diagrams, operational scenarios and sequences, andflow diagrams provided in the Figures are representative of exemplarysystems, environments, and methodologies for performing novel aspects ofthe disclosure. While, for purposes of simplicity of explanation,methods included herein may be in the form of a functional diagram,operational scenario or sequence, or flow diagram, and may be describedas a series of acts, it is to be understood and appreciated that themethods are not limited by the order of acts, as some acts may, inaccordance therewith, occur in a different order and/or concurrentlywith other acts from that shown and described herein. For example, thoseskilled in the art will understand and appreciate that a method couldalternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, not all acts illustratedin a methodology may be required for a novel implementation.

The descriptions and figures included herein depict specificimplementations to teach those skilled in the art how to make and usethe best option. For the purpose of teaching inventive principles, someconventional aspects have been simplified or omitted. Those skilled inthe art will appreciate variations from these implementations that fallwithin the scope of the invention. Those skilled in the art will alsoappreciate that the features described above can be combined in variousways to form multiple implementations. As a result, the invention is notlimited to the specific implementations described above, but only by theclaims and their equivalents.

What is claimed is:
 1. A method comprising: storing structured datarecords in a first data center, with fields of the structured datarecords correlated by one or more relational associations; responsive tochange actions comprising conflicting edits of the structured datarecords, determining relative dependencies among the change actions toestablish commutative operation providing parallel execution of changefeeds for the first data center by selective placement of the changeactions into the change feeds based at least on the relativedependencies such that first ones of the conflicting edits placed withinsame feeds employ immediate consistency verifications and second onesthe conflicting edits placed in different feeds employ delayedconsistency verifications; implementing the change actions directed tothe structured data records stored by the first data center by at leastexecuting, in the first data center, corresponding change actions inorder within each of the change feeds and in any order between thedifferent change feeds; and propagating to a second data centerreplication data comprising the change actions placed within the changefeeds for execution of the change actions by the second data center. 2.The method of claim 1, further comprising: in the second data center,receiving the replication data and implementing the change actions inparallel according to placement in the change feeds to affect thestructured data records stored by the second data center.
 3. The methodof claim 1, further comprising: for the change actions in associatedchange feeds, performing the immediate consistency verifications of thefirst ones of the change actions upon completion of each of the firstones of the change actions, and performing the delayed consistencyverifications by at least selectively delaying consistency verificationsof the second ones of the change actions until at least subsequentchange actions are performed that affect related structured datarecords.
 4. The method of claim 3, further comprising: performing firsthash comparisons for intrinsic data fields of the structured datarecords affected by the first ones of the change actions to perform theimmediate consistency verifications of the first ones of the changeactions, and delaying second hash comparisons for promoted data fieldsof the structured data records affected by the first ones of the changeactions.
 5. The method of claim 4, wherein the promoted data fieldscomprise one or more properties of other structured data records whichreference the structured data records affected by the first ones of thechange actions.
 6. The method of claim 4, further comprising: delayingthe second hash comparisons for the promoted data fields of thestructured data records affected by the first ones of the change actionsuntil after completion of the second ones of the change actions thataffect related structured data records.
 7. The method of claim 6,wherein the replication data further comprises first hash data used inthe first hash comparisons and second hash data used in the second hashcomparisons.
 8. The method of claim 4, further comprising: performingthe second hash comparisons, and if failures arise in the second hashcomparisons, ignoring the failures, and re-performing the second hashcomparisons after waiting a predetermined time.
 9. The method of claim1, further comprising: generating hash data related a state of thestructured data records after implementing the change records in thefirst data center, and including the hash data in the replication data.10. An apparatus comprising: one or more computer readable storagemedia; program instructions stored on the one or more computer readablestorage media that, when executed by a processing system, direct theprocessing system to at least: store structured data records, withfields of the structured data records correlated among by one or morerelational associations; responsive to change actions comprisingconflicting edits of the structured data records, determine relativedependencies among the change actions to establish commutative operationproviding parallel execution of change feeds for the first data centerby selective placement of the change actions into the change feeds basedat least on the relative dependencies such that first ones of theconflicting edits placed within same feeds employ immediate consistencyverifications and second ones the conflicting edits placed in differentfeeds employ delayed consistency verifications; implement the changeactions directed to the structured data records by at least executing,in the first data center, corresponding change actions in order withineach of the change feeds and in any order between the different changefeeds; and propagate replication data comprising the change actionsplaced within the change feeds for execution of the change actions by asecond data center.
 11. The apparatus of claim 10, comprising furtherprogram instructions, when executed by the processing system, direct theprocessing system to at least: implement the change actions in parallelaccording to placement in the change feeds to affect the structured datarecords.
 12. The apparatus of claim 10, comprising further programinstructions, when executed by the processing system, direct theprocessing system to at least: for the change actions in associatedchange feeds, perform the immediate consistency verifications of thefirst ones of the change actions upon completion of each of the firstones of the change actions, and perform the delayed consistencyverifications by at least selectively delaying consistency verificationsof the second ones of the change actions until at least subsequentchange actions are performed that affect related structured datarecords.
 13. The apparatus of claim 12, comprising further programinstructions, when executed by the processing system, direct theprocessing system to at least: perform first hash comparisons forintrinsic data fields of the structured data records affected by thefirst ones of the change actions to perform the immediate consistencyverifications of the first ones of the change actions, and delay secondhash comparisons for promoted data fields of the structured data recordsaffected by the first ones of the change actions.
 14. The apparatus ofclaim 13, wherein the promoted data fields comprise one or moreproperties of other structured data records which reference thestructured data records affected by the first ones of the changeactions.
 15. The apparatus of claim 13, comprising further programinstructions, when executed by the processing system, direct theprocessing system to at least: delay the second hash comparisons for thepromoted data fields of the structured data records affected by thefirst ones of the change actions until after completion of the secondones of the change actions that affect related structured data records.16. The apparatus of claim 15, wherein the replication data furthercomprises first hash data used in the first hash comparisons and secondhash data used in the second hash comparisons.
 17. The apparatus ofclaim 13, comprising further program instructions, when executed by theprocessing system, direct the processing system to at least: perform thesecond hash comparisons, and if failures arise in the second hashcomparisons, ignore the failures, and re-perform the second hashcomparisons after waiting a predetermined time.
 18. A method comprising:across a plurality of data centers, redundantly storing structured datarecords, with fields of the structured data records correlated among thestructured data records by one or more relational associations;responsive to change actions comprising conflicting edits of thestructured data records, for each data center, determining relativedependencies among the change actions to establish commutative operationproviding parallel execution of change feeds by selective placement ofthe change actions into the change feeds based at least on the relativedependencies such that first ones of the conflicting edits placed withinsame feeds employ immediate consistency verifications and second onesthe conflicting edits placed in different feeds employ delayedconsistency verifications; propagating the change actions to selecteddata centers as operations placed within each of the change feeds,wherein the selected data centers responsively implement correspondingchange actions in each of the plurality of data centers in order withineach of the change feeds and in any order between the different changefeeds to affect the structured data records stored by the selected datacenters.
 19. The method of claim 18, further comprising: for the changeactions in associated change feeds, performing the immediate consistencyverifications of the first ones of the change actions upon completion ofeach of the first ones of the change actions, and performing the delayedconsistency verifications by at least selectively delaying consistencyverifications of the second ones of the change actions until at leastsubsequent change actions are performed that affect similar structureddata records.
 20. The method of claim 19, further comprising: performingfirst hash comparisons for intrinsic data fields of the structured datarecords affected by the first ones of the change actions to perform theimmediate consistency verifications of the first ones of the changeactions, and delaying second hash comparisons for promoted data fieldsof the structured data records affected by the first ones of the changeactions, wherein the promoted data fields comprise one or moreproperties of other structured data records which reference thestructured data records affected by the first ones of the changeactions.